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Longitudinal Modelling workshop

Longitudinal studies are common in health-related areas. These studies require appropriate statistical analyses that account for the correlated nature of the data.

This two-day course provides an introduction to a range of common approaches for modelling longitudinal and correlated data. Topics covered include descriptive analysis of longitudinal data, linear mixed models, and generalised estimating equations (GEE) for modelling longitudinal data.

This course has a strong application focus. You will be learning how to implement these methods using Stata software and interpreting longitudinal results.

This course is designed for quantitative researchers/students who need to have a better understanding of longitudinal models in their work. Participants should have either completed the Regression Modelling course offered by the Biostatistics Centre or have equivalent knowledge in regression modelling for both continuous and discrete data AND have either completed the Stata course offered by the Centre or be familiar with using Stata. Please see our website for details:

Short courses – Biostatistics skill development

Please note, participants must come with their own laptop with Stata software installed, and be able to import data sets, generate new variables and be able to carry out regression analyses using Stata software. The University of Otago now offers Stata SE free to all staff and students.


Register online at the link below or contact us by email to register. Credit card payments will incur GST. Students wishing to pay by Departmental Cost Code will need to contact us by email.

Register now


Date Thursday, 7 July 2022 - Friday, 8 July 2022
Time All Day Event
Audience Public,Undergraduate students,Postgraduate students,Staff,Alumni,Allied health professionals
Event Category Health Sciences
Event Type Short Course
LocationRoom G033, Ground Floor, Adams Building, 18 Frederick Street, Dunedin
Cost$350 early bird registration before 27 June 2022
Contact NameBiostatistics administration
Contact Phone+64 3 479 7223

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